Introduction
Structured Query Language (SQL) remains a cornerstone of data handling and analysis in the data science field. SQL is indispensable whether you are querying a relational database, cleaning datasets, or performing exploratory data analysis. Writing efficient and effective SQL queries is not just a technical skill—it is a critical thinking process that can greatly impact the quality and speed of your insights. This article will explore strategies for writing better SQL queries, especially tailored for those involved in or preparing for a Data Science Course in mumbai.
Understand the Business Question First
Before typing a single line of SQL, it is crucial to understand the problem you are solving thoroughly. This may sound basic, but many inefficient queries stem from unclear objectives. Ask yourself:
- What information is needed?
- Which tables contain this data?
- What are the key metrics or dimensions?
By defining the question first, you ensure that your SQL query is syntactically correct and contextually relevant. Many advanced-level data course curricula emphasise this skill early on to build a solid analytical foundation.
Know Your Database Schema
Understanding how your data is structured is essential for efficient querying. Knowing which tables exist, how they relate to one another (primary and foreign keys), and what each column represents can help avoid unnecessary joins or subqueries. Use schema diagrams or data dictionaries when available. If your database contains millions of rows, even a single unnecessary join can slow down performance considerably.
Tools like PostgreSQL’s EXPLAIN or MySQL’s EXPLAIN ANALYZE can help you understand how your query will be executed, revealing performance bottlenecks or poor indexing.
Use Explicit Columns Instead of SELECT *
The temptation to use SELECT * is understandable, especially in the exploratory phase. However, selecting all columns:
- Increases data transfer time.
- Makes the query harder to read.
- Can break downstream applications if the schema changes.
Always specify the columns you need. This not only makes your intent clearer but also improves query performance. This best practice must be adopted by all practitioners and professionals, as it encourages precision and resource efficiency.
Leverage Indexes Wisely
Indexes constitute the best option for improving read performance. Make sure to:
Query on indexed columns whenever possible.
Avoid operations that prevent index usage, like applying functions to columns in WHERE clauses (WHERE YEAR(date_column) = 2024 is bad; use BETWEEN instead).
Before you make changes to the schema, consult your DBA or check your platform’s indexing strategy. Improper indexing can hurt performance as much as no indexing at all.
Filter Early, Join Later
Filtering data as early as possible in your query can drastically improve performance. If you know you only need data from 2023, apply that filter before joining with other tables:
WITH filtered_sales AS (
SELECT * FROM sales WHERE sale_date >= ‘2023-01-01’
)
SELECT *
FROM filtered_sales s
JOIN customers c ON s.customer_id = c.id;
This common technique is taught in many Data Scientist Course projects, where handling large datasets efficiently is a key learning objective.
Use CTEs and Subqueries for Readability
Common Table Expressions (CTEs) and subqueries can make your SQL much more readable, especially for complex analyses. Rather than writing one massive query, break it into smaller parts:
WITH customer_orders AS (
SELECT customer_id, COUNT(*) AS order_count
FROM orders
GROUP BY customer_id
),
high_value_customers AS (
SELECT customer_id
FROM customer_orders
WHERE order_count > 10
)
SELECT *
FROM customers
WHERE id IN (SELECT customer_id FROM high_value_customers);
Readable queries are easier to debug, maintain, and explain—critical skills in any team-based data science environment.
Optimise Joins
Joins are among the most expensive operations in SQL. To make them more efficient:
- Use INNER JOINs instead of OUTER JOINs unless necessary.
- Join on indexed columns.
- Reduce row counts before performing joins.
Also, check for Cartesian products caused by missing join conditions—these are a common source of massive, unnecessary datasets.
Watch Out for NULLs
NULL values can be a cause of concern even for seasoned SQL users. Be careful when using = or != comparisons, as they do not behave as expected with NULLs. Use IS NULL or IS NOT NULL to handle missing data properly.
— Incorrect
SELECT * FROM customers WHERE referral_code != NULL;
— Correct
SELECT * FROM customers WHERE referral_code IS NOT NULL;
Accounting for NULLs is critical, especially when dealing with real-world, imperfect data.
Aggregate with Care
Aggregations are powerful but can become performance drains if misused. Here are some guidelines:
- Use GROUP BY on indexed columns where possible.
- Avoid aggregating more data than needed—filter before aggregating.
- Consider using window functions rather than self-joins or subqueries when calculating rolling metrics.
Window functions like ROW_NUMBER(), RANK(), and SUM() OVER() can be more efficient and readable than traditional aggregation workarounds.
Document Your SQL
Comment your SQL queries just as you would with code. Use — for inline comments and /* … */ for block comments. This helps teammates (or future you) understand why the query was written a certain way.
— Get top 10 products by revenue in Q1 2024
SELECT product_id, SUM(sales_amount) AS revenue
FROM sales
WHERE sale_date BETWEEN ‘2024-01-01’ AND ‘2024-03-31’
GROUP BY product_id
ORDER BY revenue DESC
LIMIT 10;
Well-documented SQL is a hallmark of professional practice, and it demonstrates the technical excellence and skills of a data professional.
Validate and Test Your Queries
Before relying on query results, test on a small dataset or use LIMIT to ensure logic correctness. Consider edge cases, like users with no orders or products with zero sales. Cross-check results with other tools or manual calculations when possible.
Even if a query is syntactically correct, it will generate inaccurate results if the logic is flawed.
Use Tools and Version Control
Modern data scientists benefit from tools like dbt, Jupyter Notebooks with SQL magics, or integrated SQL IDEs (like DataGrip or DBeaver). These tools allow for version control, testing, and modular SQL development.
Storing your SQL scripts in Git enables collaboration, change tracking, and rollback—critical for reproducibility in data science projects.
Conclusion
Mastering SQL is an ongoing journey that blends technical skills with analytical thinking. As data continues to drive decision-making across industries, the ability to write clean, efficient, and accurate SQL queries becomes a competitive advantage for any data professional.
Whether you are a self-taught data practitioner or a working professional, practicing these principles will elevate your SQL proficiency and make your analyses faster, more reliable, and easier to interpret. SQL is not just about getting the data—it is about asking the right questions and crafting queries that reveal meaningful insights.
Business Name: ExcelR- Data Science, Data Analytics, Business Analyst Course Training Mumbai
Address: Unit no. 302, 03rd Floor, Ashok Premises, Old Nagardas Rd, Nicolas Wadi Rd, Mogra Village, Gundavali Gaothan, Andheri E, Mumbai, Maharashtra 400069, Phone: 09108238354, Email: enquiry@excelr.com.